• 스마트미디어저널
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• 제10권3호
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• pp.9-22
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• 2021

Basically, machine learning models use input data to produce results. Sometimes, the input data is too complicated for the models to learn useful patterns. Therefore, feature engineering is a crucial data preprocessing step for constructing a proper feature set to improve the performance of such models. One of the most efficient methods for automating feature engineering is the autoencoder, which transforms the data from its original space into a latent space. However certain factors, including the datasets, the machine learning models, and the number of dimensions of the latent space (denoted by k), should be carefully considered when using the autoencoder. In this study, we design a framework to compare two data preprocessing approaches: with and without autoencoder and to observe the impact of these factors on autoencoder. We then conduct experiments using autoencoders with classifiers on popular datasets. The empirical results provide a perspective regarding the best suited autoencoder for these factors.

• Communications for Statistical Applications and Methods
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• 제31권3호
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• pp.279-289
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• 2024

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying meta-classifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.

• 한국인터넷방송통신학회논문지
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• 제16권2호
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• pp.137-144
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• 2016

본 연구의 목적은 원격평생교육에서 학습자의 목표지향성, 교수실재감, 학습접근방식, 만족도 및 학업성취도 간의 구조적 관계를 규명하는 것이다. 이를 위해 A, B, C 대학교 부설 원격평생교육원 학습자 235명을 대상으로 온라인 설문조사를 실시하였다. 구조방정식 모델링 분석을 통한 연구 결과, 첫째, 숙달접근목표지향성과 교수실재감은 심층학습접근에 정적 영향을 미쳤으며, 둘째, 숙달접근목표지향성은 피상학습접근에 부적 영향을, 교수실재감에는 영향을 미치지 않는 것으로 확인되었다. 셋째, 심층학습접근은 만족도에 정적영향을 미쳤으며, 넷째, 피상학습접근은 만족도에 부적영향을 미쳤다. 다섯째, 심층학습접근은 학업성취도에 정적영향을 미쳤으며, 여섯째, 피상학습접근은 학업성취도에 부적영향을 미치는 것으로 확인되었다. 위와 같은 연구결과는 학습자의 숙달접근목표지향성과 교수실재감이 학습자들로 하여금 심층학습접근방식을 선택하게 하여 궁극적으로는 학업성취도와 만족도를 높여줌을 시사하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

• 한국언어정보학회지:언어와정보
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• 제10권1호
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• pp.21-46
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• 2006

This study proposes a Word Sense Disambiguation (WSD) algorithm, based on concept learning with special emphasis on statistically meaningful lowest frequency words. Previous works on WSD typically make use of frequency of collocation and its probability. Such probability based WSD approaches tend to ignore the lowest frequency words which could be meaningful in the context. In this paper, we show an algorithm to extract and make use of the meaningful lowest frequency words in WSD. Learning method is adopted from the Find-Specific algorithm of Mitchell (1997), according to which the search proceeds from the specific predefined hypothetical spaces to the general ones. In our model, this algorithm is used to find contexts with the most specific classifiers and then moves to the more general ones. We build up small seed data and apply those data to the relatively large test data. Following the algorithm in Yarowsky (1995), the classified test data are exhaustively included in the seed data, thus expanding the seed data. However, this might result in lots of noise in the seed data. Thus we introduce the 'maximum a posterior hypothesis' based on the Bayes' assumption to validate the noise status of the new seed data. We use the Naive Bayes Classifier and prove that the application of Find-Specific algorithm enhances the correctness of WSD.

• 아태비즈니스연구
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• 제11권3호
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• pp.261-271
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• 2020

Purpose - This study was to establish a unified tourism management class for university students based on Gangwon-do. Community based learning(CBL) was applied to provide a tangible and intangible resource of tourism resources the theoretical approaches and the actual experiences of the community. Design/methodology/approach - In order to design a unified tourism management module, this study applied qualitative research and quantitative research methods to collect information on the direction of the module. the study conducted in-depth interviews and then an online survey. Findings - According to the results of the study, the main parts should include necessity of unification, inter-Korean tourism, inter-Korean cooperation, inter-Korean economy, and international relations. Research implications or Originality - The overall composition of the unification tourism management class should be designed as the unification tourism management theory to acquire the subject knowledge, the field trip to the border area for experiential learning, and the assignment of the field study task to understand the community.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• 의학교육논단
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• 제23권3호
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• pp.194-203
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• 2021

Learning communities in medical education have demonstrated favorable outcomes in terms of students' learning, professional development, and wellness. Despite these strengths and the widespread adoption of learning communities in US medical schools, there has been little interest in medical learning communities in Korea. In this context, the present study examined the development and implementation of the Yonsei Medical Learning Community (YMLC) and analyzed its outcomes and areas of improvement. The Yonsei University College of Medicine has operated a learning community as part of the undergraduate medical education curriculum since 2014. The YMLC is the first program of its type in Korea. The overall structure of the YMLC consists of four distinct communities (pillars), which are named after four distinguished alumni, and each pillar is organized into five learning community classes. Each class is vertically integrated across students in different medical school years, and one faculty advisor is matched to about 30 students. As the YMLC focuses on fostering reflective practice in students and providing them with opportunities to build teamwork and experience social relatedness, two educational approaches have been adopted: reflective writing and mentoring and community activities. In this study, we obtained and analyzed second-year students' feedback on the YMLC curriculum and identified its achievements, merits, and areas that need improvement. The results have shown that over 75% and 60% of respondents reported satisfaction with reflective writing and mentoring and community activities, respectively. The educational activities of the learning community helped students regularly reflect on their learning and progress and establish close relationships with faculty advisors. However, several areas of improvement regarding content, format, and logistical issues were also identified. The present findings may provide valuable information for other institutions to develop learning communities relevant to their own context.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.171-183
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• 2020

Capsule endoscopy is one of the increasingly demanded diagnostic methods among patients in recent years because of its ability to observe small intestine difficulties. It is often conducted for 12 to 14 hours, but significant frames constitute only 10% of whole frames. Thus, it has been designed to automatically acquire significant frames through deep learning. For example, studies to track the position of the capsule (stomach, small intestine, etc.) or to extract lesion-related information (polyps, etc.) have been conducted. However, although grouping or labeling the training images according to similar features can improve the performance of a learning model, various attributes (such as degree of wrinkles, presence of valves, etc.) are not considered in conventional approaches. Therefore, we propose a class-labeling method that can be used to design a learning model by constructing a knowledge model focused on main lesions defined in standard terminologies for capsule endoscopy (minimal standard terminology, capsule endoscopy structured terminology). This method enables the designing of a systematic learning model by labeling detailed classes through differentiation of similar characteristics.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.213-219
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• 2023

In recent years, popularity of deep learning (DL) is increased due to its ability to extract features from Hyperspectral images. A lack of discrimination power in the features produced by traditional machine learning algorithms has resulted in poor classification results. It's also a study topic to find out how to get excellent classification results with limited samples without getting overfitting issues in hyperspectral images (HSIs). These issues can be addressed by utilising a new learning network structure developed in this study.EfficientNet-B4-Based Convolutional network (EN-B4), which is why it is critical to maintain a constant ratio between the dimensions of network resolution, width, and depth in order to achieve a balance. The weight of the proposed model is optimized by Search and Rescue Operations (SRO), which is inspired by the explorations carried out by humans during search and rescue processes. Tests were conducted on two datasets to verify the efficacy of EN-B4, with Indian Pines (IP) and the University of Pavia (UP) dataset. Experiments show that EN-B4 outperforms other state-of-the-art approaches in terms of classification accuracy.